Senior Artificial Intelligence Engineer

About the role

ARRAY, a software development company based in Bahrain, is growing its Quant Dev team and is looking for a Senior AI Engineer. The team turns multi-modal data into decisions for enterprise-scale products, working where research meets production. The role takes an AI product from requirement to production and leads the build, combining strong mathematical and machine learning foundations with high-standard software engineering.

Key responsibilities

• Own the model and system choices in deployed products (classical ML, LLM, retrieval and agent systems), covering selection, training or prompting, evaluation, deployment, monitoring and improvement.
• Design, build, deploy and operate production-grade software at enterprise scale, owning features end to end, on cloud or on-premises.
• Assemble and lead junior engineers: plan the work, set the design, review code, unblock and mentor.
• Raise standards through code review, testing, sound design and expert use of AI coding agents.
• Work with the commercial team to understand requirements, design the solution and estimate effort and risk.
• Contribute to the ideation and shaping of AI-powered products.

Required profile

• At least 5 years of hands-on experience building and deploying software products, including several years delivering AI/ML systems to production.
• Depth in classical machine learning or in LLM systems (retrieval, RAG, agents, evaluation, fine-tuning).
• Strong mathematical and statistical foundations (probability, statistics, linear algebra, optimisation).
• Experience leading other engineers on a delivery.
• Able to engage with the commercial team and communicate well verbally and in writing.
• Master's or Ph.D. in Computer Science, Data Science, Mathematics, AI or a related quantitative field, or a Bachelor's with equivalent applied depth.

Required skills

• Python: system design, testing, version control, code review and CI/CD.
• Nice to have: MLOps and LLM evaluation tooling, multi-modal data, Docker, Kubernetes, cloud platforms (AWS, Azure, GCP), regulated-domain experience and startup culture.